惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

T
Tailwind CSS Blog
The GitHub Blog
The GitHub Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
B
Blog
Microsoft Security Blog
Microsoft Security Blog
Stack Overflow Blog
Stack Overflow Blog
量子位
Martin Fowler
Martin Fowler
月光博客
月光博客
P
Proofpoint News Feed
博客园_首页
Y
Y Combinator Blog
I
InfoQ
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
H
Help Net Security
U
Unit 42
GbyAI
GbyAI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 司徒正美
MongoDB | Blog
MongoDB | Blog
F
Fortinet All Blogs
罗磊的独立博客
酷 壳 – CoolShell
酷 壳 – CoolShell

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
A Randomly Expandable Method for Data Layout of RAID Stor...
De-zhai Yuan, Xing-yi Peng, Ting Liu, Zhe Cui · 2017-09-14 · via cs.DC updates on arXiv.org

With the increase of huge amounts of data in volume, velocity, and variety, the need for capacity of Redundant Arrays of Inexpensive Disks (RAID) storage systems is dramatically growing. However, the probability of disk failures in RAID storage systems is sharply high with the increase of program/erase cycles, read cycles, and retention time. Furthermore, they are faced with more challenges in fault tolerance, storage efficiency, computational complexity, and expandability. This article presents a novel data layout scheme for RAID storage System using Random Binary Extensive Code (RBEC), which are designed to ensure random expandability, high reliability, and availability of data in RAID storage systems. RBEC is a family of systematic code, in which the generator matrix consists of two submatrices with entries over GF(2), an identity matrix on the top, and another submatrix on the bottom. Compared with the existed approaches, the attractive advantages of our schemes include 1) they are completely implemented based on only simple eXclusive OR (XOR) operations and have systematic code property, 2) they can provide arbitrary fault tolerance, 3) their storage efficiency is quasi-optimal, and 4) data and parity disks of RAID storage systems can be randomly expanded according to practical requirements. Thus, our scheme is particularly suitable for RAID storage systems that need higher reliability, availability, and expandability.